The ICP for Fragment Identification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F12%3APU99372" target="_blank" >RIV/00216305:26210/12:PU99372 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
The ICP for Fragment Identification
Original language description
In technical practice we are very often confronted with need to approximate functions from measured values. Another frequent task is a calculation of measure of central tendency of sample data. For a good reason the method of least squares and the statistics like mean or median are being used. The goal of this paper is to show some nonstandard metrics usable in tasks of creation of approximation model or in tasks of symbolic regression. These metrics, as will be shown, can be created using so-called generating function. It is important to note these metrics can affect robustness of created model concerning extremely deviated values. Using these exotic metrics in tasks of data approximation or symbolic regression we get nonlinear unconstrained optimization task. To solve such task it is necessary to use adequate optimization strategies such as soft-computing methods (evolution algorithms, HC12, differential evolution, etc.) or classical methods of nonlinear optimization (Nelder-Mead, co
Czech name
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Czech description
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Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
18th International Conference of Soft Computing, MENDEL 2012
ISBN
978-80-214-4540-6
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
588-593
Publisher name
VUT
Place of publication
Brno
Event location
Brno University of Technology
Event date
Jun 27, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000288144100072

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